Master of Science

thesisDynamic contrast-enhanced magnetic resonance imaging can be useful to quantify cardiovascular disease. However, patient respiratory motion can affect the accuracy of subsequent analyses, making the quantification poor. Motion correction techniques are always an extra challenge for dynamic contrast-enhanced data since the intensity changes rapidly over time. In the case of myocardial perfusion, there is respiratory motion, cardiac motion, and the patient can move. All these things combined make the problem difficult to solve. Most cardiac perfusion sequences acquire multiple two-dimensional (2D) slices. Several three-dimensional (3D) methods have been published, but they have employed breath-holding and no registration in the postprocessing steps for quantification. To address this challenge, we present a new motion correction technique that uses rigid and nonrigid registration methods for removing motion from 3D time series data. The 3D acquisition has the advantage that out-of-plane motion can be registered (though not out-of-slab motion). This method is unique because it employs a 3D image registration that not only resolves in-plane motion but also resolves the out-of-plane motion in a 3D volume. This method was evaluated to observe shifts and flow values with the ground truth. Applying this method ensures smooth registration between all time frames of the data, giving a more accurate analysis of the flow values for quantification of myocardial perfusion